metricMCB.cv: Calculation of model AUC for Methylation Correlation Blocks...

In whirlsyu/EnMCB: Predicting Disease Progression Based on Methylation Correlated Blocks using Ensemble Models

Calculation of model AUC for Methylation Correlation Blocks using cross validation

Description

To enable quantitative analysis of the methylation patterns within individual Methylation Correlation Blocks across many samples, a single metric to define the methylated pattern of multiple CpG sites within each block. Compound scores which calculated all CpGs within individual Methylation Correlation Blocks by SVM model were used as the compound methylation values of Methylation Correlation Blocks.

Usage

metricMCB.cv(MCBset,data_set,Surv,nfold,
Method,predict_time,alpha,n_mstop,n_nu,theta,silent)

Arguments

MCBset	Methylation Correlation Block information returned by the IndentifyMCB function.
data_set	methylation matrix used for training the model in the analysis.
Surv	Survival function contain the survival information for training.
nfold	fold used in the cross validation precedure.
Method	model used to calculate the compound values for multiple Methylation correlation blocks. Options include "svm", "cox", "mboost", and "enet". The default option is SVM method.
predict_time	time point of the ROC curve used in the AUC calculations, default is 3 years.
alpha	The elasticnet mixing parameter, with 0 <= alpha <= 1. alpha=1 is the lasso penalty, and alpha=0 the ridge penalty. It works only when "enet" Method is selected.
n_mstop	an integer giving the number of initial boosting iterations. If mstop = 0, the offset model is returned. It works only when "mboost" Method is selected.
n_nu	a double (between 0 and 1) defining the step size or shrinkage parameter in mboost model. It works only when "mboost" Method is selected.
theta	penalty used in the penalized coxph model, which is theta/2 time sum of squared coefficients. default is 1. It works only when "cox" Method is selected.
silent	Ture indicates that processing information and progress bar will be shown.

Value

Object of class list with elements (XXX will be replaced with the model name you choose):

MCB_matrix	Prediction results of model.
auc_results	AUC results for each model.

Author(s)

Xin Yu

References

Xin Yu et al. 2019 Predicting disease progression in lung adenocarcinoma patients based on methylation correlated blocks using ensemble machine learning classifiers (under review)

Examples

#import datasets
data(demo_survival_data)
datamatrix<-create_demo()
data(demo_MCBinformation)
#select MCB with at least 3 CpGs.
demo_MCBinformation<-demo_MCBinformation[demo_MCBinformation[,"CpGs_num"]>2,]

trainingset<-colnames(datamatrix) %in% sample(colnames(datamatrix),0.6*length(colnames(datamatrix)))
testingset<-!trainingset
#create the results using Cox regression. 
mcb_cox_res<-metricMCB.cv(MCBset = demo_MCBinformation,
               data_set = datamatrix,
               Surv = demo_survival_data,
               Method = "cox")

whirlsyu/EnMCB documentation built on Jan. 25, 2023, 4:33 a.m.